Project - Computer Vision #2 Part 2

                         by ARYAN JAIN

SUMMARY

Context: Company X intends to build a face identification model to recognise human faces.

Data Description:
The dataset comprises of images and its mask where there is a human face

Domain: Face recognition

Objectives: Face Aligned Face Dataset from Pinterest. This dataset contains 10,770 images for 100 people. All images are taken from 'Pinterest' and aligned using dlib library

Key Tasks: Use a pre-trained model trained on Face recognition to recognise similar faces. The intent is to recognise whether two given faces are of the same person or not.

- Load the dataset and create the metadata.
- Check some samples of metadata.
- Load the pre-trained model and weights.
- Generate Embedding vectors for each face in the dataset.
- Build distance metrics for identifying the distance between two given images.
- Use PCA for dimensionality reduction.
- Build an SVM classifier in order to map each image to its right person.
- Import the the test image. Display the image. Use the SVM trained model to predict the face.
Importing Libraries
training data location

Strategy

Below is the VGG Face model (reference source >>> internet )

VGG_Face.PNG

reference from internet:

$ d(p,q) = \sqrt{\sum \limits_{i=1}^{n} (q_{i} - p_{i}) ^2} $

p,q = two points in Euclidean n-space
$q_{i}, p_{i} $ = Euclidean vectors, starting from the origin of the space (initial point)
n = n-space

Getting ready for model building, training & validating
Split the dataset into training and validation
Problem statement requires to use PCA for dimensionality reduction
SVM to predict

8.1 Predict on test images

END OF PROJECT PART 2